Data compression by using cognitive created dictionaries

ABSTRACT

A compression method, system, and computer program product include creating compressed data via a first system from input data, sending information to a second system detailing a compression strategy for the compressed data, and learning, via the second system, from the information how to recreate the input to the first system using the compressed data.

BACKGROUND

The present invention relates generally to a cognitive data compressionmethod applicable to a cloud computing environment, and moreparticularly, but not by way of limitation, to a system, method, andcomputer program product for data compression by creating compresseddata of a minimum size with an effort level of decoding, where theeffort level is dependent on the number of computing steps of the targetdecoding system.

Compression is conventionally performed by software programs that use aformula or algorithm to determine how to shrink the size of the data.For example, an algorithm may represent a string of bits, or 0's and1's, with a smaller string of 0's and 1's by using a dictionary for theconversion between them, or the formula may insert a reference orpointer to a string of 0s and 1s that the program has already seen.Also, compressing data can be a lossless or lossy process in thatlossless compression enables the restoration of a file to its originalstate, without the loss of a single bit of data, when the file isuncompressed and lossy compression permanently eliminates bits of datathat are redundant, unimportant or imperceptible.

However, the conventional lossless data compression is based on recodingthe data to reduce redundancy based on the Shanon Theorem. Theredundancy is a formal redundancy looking at the used symbols and doesnot take in account that there is also redundancy based on the context.

SUMMARY

In an exemplary embodiment, the present invention can provide acomputer-implemented compression method, the method including creatingcompressed data via a first system from input data, sending informationto a second system detailing a compression strategy for the compresseddata, learning, via the second system, from the information how torecreate the input to the first system using the compressed data. One ormore other exemplary embodiments include a computer program product anda system.

Other details and embodiments of the invention will be described below,so that the present contribution to the art can be better appreciated.Nonetheless, the invention is not limited in its application to suchdetails, phraseology, terminology, illustrations and/or arrangements setforth in the description or shown in the drawings. Rather, the inventionis capable of embodiments in addition to those described and of beingpracticed and carried out in various ways that should not be regarded aslimiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention will be better understood from the followingdetailed description of the exemplary embodiments of the invention withreference to the drawings, in which:

FIG. 1 exemplarily shows a high-level flow chart for a compressionmethod 100 according to an embodiment of the present invention;

FIGS. 2A-B exemplarily depicts a cognitive learning system with twosubsystems according to an embodiment of the present invention;

FIG. 3 depicts a cloud-computing node 10 according to an embodiment ofthe present invention;

FIG. 4 depicts a cloud-computing environment 50 according to anembodiment of the present invention; and

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-5, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawings are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity.

By way of introduction of the example depicted in FIG. 1, an embodimentof a cognitive data compression method 100 according to the presentinvention can include two subsystems in a feedback loop, which representthe encoder, which converts original data to compressed data(compressor), and a decoder, which converts the compressed data back tothe original data (decompressor), where the subsystems are trainedwithout training data/pre-annotated data. The input to the compressor iscompared to the output of the decompressor, where the result should bethe same. The subsystems compress as much data as possible, while stillresulting in the same output, so that both subsystems are continuouslytraining/learning from each other (rather than learning from providedtraining examples) and providing feedback to each other to improve thecompression rate.

By way of introduction of the example depicted in FIG. 3, one or morecomputers of a computer system 12 according to an embodiment of thepresent invention can include a memory 28 having instructions stored ina storage system to perform the steps of FIG. 1.

With reference to FIGS. 1 and 2A-B, in step 101, compressed data iscreated via a first system 210 from input data. The first system 210 caninclude an encoder which converts the original input data to thecompressed data. Entropy compression algorithms could be part of thecompression (e.g., as a post processing step after cognitivecompression).

In step 102, a size of the compressed data is measured.

In step 103, information (e.g., such as a decoding hint) is sent as metadata along with the compressed data to a second system 220 detailing acompression strategy for the compressed data. That is, the first system210 sends information to the second system 220 about details of thecompression as decoding hints. For example, grammar and spelling rulesused to compress the data by the first system 210 can be sent to thesecond system 220 and can be replaced by shape recognition (e.g., thesystem can learn how to detect a car or house and how to parameterize itmost efficiently for the given type of picture like paintings orphotos). Or, in videos, movement can be used as a context for thecompression (e.g., if cars are observed on a junction the cars alwaysmove in a defined direction related to the position of the street. Aviolation of the general rule can be handle as a special compressioncase. If the data will be analyzed later this special compression casecan be use of events of special interest for e.g. the police to find aspecial event faster than to watch the whole video from the beginning).Or, for example, a cognitive approach knows that a word is a verb,adjective, noun, etc. and the grammar context. Hence, the informationfor the compressed can reduce a phrase to its basic information and adda tag describing the grammar context. The language or dictionary can besent as the information but the dictionary can be trained over time fromthe information. Thereby, this increases the redundancy of the processestext because many different words where translated to their basic formwhile the grammar context makes it possible to convert the same basicword to different derived forms based on the grammar context.

In step 104, via the second system 220, is it learned from theinformation how to recreate the input from the first system 210 usingthe compressed data. The compressed data is processed by the secondsystem 220 using the information to create the original data (i.e., theinput).

In step 105, a quality of the recreated input is measured by the secondsystem 220 to send a feedback to the first system 210 to adjust theinformation for the compressed strategy. The second system 220 sendsfeedback to the first system 210 about the effort of decoding (e.g., ifthe effort is very high or it was impossible to create unambiguousoriginal data using the information sent). That is, the output from thesecond system 220 is compared to the input to the first system 210.Depending on the quality (i.e., the difference between the input andoutput), feedback is sent to the first system 210 about how the secondsystem 220 was unable (or able) to decode the compressed data. In otherwords, the first system 210 teaches the second sub-system how to decodethe meta data using the information as a hint while the second system220 teaches the first system 210 through the feedback about the qualityof the teaching. Thereby, there is a dual channel teaching between boththe compressor and de-compressor to optimize the size of the compresseddata.

In step 106, steps 101-105 are repeated to confirm that the modifiedcompression strategy creates the compressed data of a minimum size bycomparing the new size with the new compress strategy to the previoussize with the previous compression strategy. That is, after the firstsystem 210 receives a feedback from the second system 220, the firstsystem 210 updates the information used for compressing the input to thecompressed data and sends the updated information. The second system 220uses the newly compressed data and the updated information to decompressthe compressed data. The quality is measured and if the quality exceedsa predetermined threshold, the information does not need to be sentanymore to the second system 220 since the second system 220 already haslearned the information used to decompress the compressed data.

That is, the information is continuously sent until the second system220 has an optimal decompression rate of the compressed data then thesecond system 220 no longer needs the information since the informationis already known. Until new information is used to compress the data, nonew information needs to be sent to the second system 220. Therefore,when the feedback loop reaches its optimal point the training processwill stop with the result of a dictionary of metadata to compress data.This dictionary of metadata (i.e., the information) will further be usedto compress/decompress new data streams such that the information doesnot need to be continuously sent with the compressed data to the secondsystem 220.

Thus, compressed data of minimum size with reasonable effort of decodingcan be created. The meaning of reasonable depends on the use case (e.g.,the number of reasonable computing steps will be different if the targetdecoding system is a mainframe, a PC or a mobile device). For example,if the system should be used to compress the data of a library forscientific astronomical publications (e.g., first information is thepublications used to compress the data), it must be trained withexisting data of this class (e.g., the publications are transmitted tothe second system 220 as the hints for decoding). New publications aboutthis type of information (scientific astronomical publications) will beprocessed very efficiently. Or, if the second system 220 cannotunderstand the new types of scientific publications because the qualityis below a certain level, the feedback necessitates that the newinformation is sent with the compressed data to update the second system220.

That is, in an operational process as depicted in FIG. 2B, theinformation no longer needs to be sent with the compressed data. Eachlocation (A/B) already “knows” the dictionary forcompressing/decompressing data and only the compressed data needs to besent.

If the method must learn the processing of new data it can use theresults from related learned processes (e.g., if the system has learnedto process scientific astronomical publications it can use thisinformation as a good starting point to learn the compression of otherscientific publications in physics or of publications in popular sciencemagazines). The other publications can be sent as part of additionalinformation as the hints to decompress the compressed data.

In one exemplary embodiment, the first system 210 can compress thephrase “Smoking is dangerous because smoke is dangerous but I smokedyesterday a lot.” Cognitive processing is used increase redundancy whatimproves the compression rate (e.g., only the words derived from smoke)and the information of the language used is sent to the second system220. Thus, the phrase is reduced to “smoke is dangerous (Gerund tag)because smoke is dangerous (present tense tag) but I smoke yesterday alot (past tense tag)”. The second system 220 can receive the informationabout the language and grammar rules and create the original text(i.e., 1) smoke is Smoking because it is the first word in the phraseand it is a gerund, 2) smoke is smoke because it present tense, and 3)smoke is smoked because it is past tense).

In another exemplary embodiment, for movie compression, a car is drivingon the street and there is a traffic light. In cognitive compression, aslong the traffic light is green the car will not change its speedsignificantly. If the traffic light is changing to yellow and then redthe call will reduce its speed until it stays with a probability ofnearly 100%. These rules are sent with the compressed data as theinformation or the second system 220 to decompress the data into theoriginal input.

In other words, the method 100 can provide a first system 210 applyingmetadata to a process to create an output from an input and a secondsystem 220 learns from the metadata how to recreate the input from thefirst system 210 using the output. Or instead recreating the input datahow to detect hidden information not known to the second system butknown in the total system, especially the measurement unit. Ameasurement unit to measure the quality of the recreated inputdata/detected hidden information. The feedback sub-system teaches thefirst sub-system based on its knowledge of the measured quality,metadata, input and output data. The data can be a set of unstructureddata of a special type like a text book about physics (category 1),chemistry (category 2), surveilling videos from a street (category 3)pictures from people in the forest (category 4), etc. The systems canbe, for example, neuronal networks or cognitive expert systems likeWatson. The systems can use an external data source like pre-defineddata or the internet. The external data source enables the system to usea good starting point for the training feedback loop.

Thereby, the above embodiments can provide a method for cognitive datacompression, by creating compressed data of minimum size with an effortlevel of decoding, where the effort level is dependent on the number ofcomputing steps of the target decoding system (i.e. PC, mainframe havemore steps than a mobile device). The method includes at least twosystems in a feedback loop, which represent the encoder, which convertsoriginal data to compressed data (compressor), and a decoder, whichconverts the compressed data back to the original data (decompressor),where the systems are trained without training data/pre-annotated data.The input to the compressor is compared to the output of thedecompressor, where the result should be the same. The systems compressas much data as possible, while still resulting in the same output, sothat both subsystems are continuously training/learning from each other(rather than learning from provided training examples) and providingfeedback to each other to improve the compression rate.

Exemplary Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of distributed computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 3, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablenode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth herein.

Although cloud computing node 10 is depicted as a computer system/server12, it is understood to be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with computersystem/server 12 include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop circuits, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems orcircuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

Referring now to FIG. 3, a computer system/server 12 is shown in theform of a general-purpose computing circuit. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further described below, memory 28 mayinclude a computer program product storing one or program modules 42comprising computer readable instructions configured to carry out one ormore features of the present invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may be adapted for implementation in anetworking environment. In some embodiments, program modules 42 areadapted to generally carry out one or more functions and/ormethodologies of the present invention.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing circuit, other peripherals,such as display 24, etc., and one or more components that facilitateinteraction with computer system/server 12. Such communication can occurvia Input/Output (I/O) interface 22, and/or any circuits (e.g., networkcard, modem, etc.) that enable computer system/server 12 to communicatewith one or more other computing circuits. For example, computersystem/server 12 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 20. As depicted,network adapter 20 communicates with the other components of computersystem/server 12 via bus 18. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system/server 12. Examples, include, but arenot limited to: microcode, circuit drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 4 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 5, an exemplary set of functional abstractionlayers provided by cloud computing environment 50 (FIG. 4) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 5 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and compression method 100 in accordance withthe present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), a Storage Area Network (SAN), a Network AttachedStorage (NAS) device, a Redundant Array of Independent Discs (RAID), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a USB “thumb”drive, a mechanically encoded device such as punch-cards or raisedstructures in a groove having instructions recorded thereon, and anysuitable combination of the foregoing. A computer readable storagemedium, as used herein, is not to be construed as being transitorysignals per se, such as radio waves or other freely propagatingelectromagnetic waves, electromagnetic waves propagating through awaveguide or other transmission media (e.g., light pulses passingthrough a fiber-optic cable), or electrical signals transmitted througha wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computer-implemented compression method, themethod comprising: creating compressed data via a first system frominput data; sending information to a second system detailing acompression strategy for the compressed data; learning, via the secondsystem, from the information how to recreate the input to the firstsystem using the compressed data; decompressing, via the second system,the compressed data to a recreated input; comparing the input data tothe first system with the recreated input in an iterative loop; based ona result of the comparing and the iterative loop of the comparing,modifying the information at each iterative loop how to recreate theinput such that the input data to the first system matches the recreatedinput; and sending a feedback to the first system based on the recreatedinput by the second system, wherein the first system teaches the secondsystem how to recreate the input simultaneously while the second systemteaches the first system an effectiveness of the teaching that the firstsystem provides to the second system, further comprising measuring aquality of the recreated input by the second system to send a feedbackto the first system to adjust the information for the compressionstrategy, wherein the information is not sent to the second system witha next compressed data when the quality is greater than a predeterminedthreshold value, wherein the first system and the second system aretrained by the learning and feedback without training data and/orpre-annotated data, wherein the input data comprises a set ofunstructured data of special types having multiple categories toclassify each of the special types into one of the multiple categories,and wherein the first system and the second system comprise a neuronalnetwork or a cognitive expert.
 2. The computer-implemented method ofclaim 1, further comprising: measuring a size of the compressed data;and if the compression strategy is modified, repeating the creating, thelearning, and the sending to confirm that the modified compressionstrategy creates compressed data including a minimum size by comparing anew size to the measured size.
 3. The computer-implemented method ofclaim 1, wherein the compressed data is created with a minimum size withan effort level of decoding, the effort level of decoding is dependenton a number of computing steps of the second system.
 4. Thecomputer-implemented method of claim 1, wherein the feedback is sent tothe first system if the recreated input and the input do not match. 5.The computer-implemented method of claim 1, further comprising: if thecompression strategy is modified, repeating the creating, the learning,and the sending to confirm that the modified compression strategycreates compressed data including a minimum size by comparing a new sizeto the measured size, wherein the information is not sent to the secondsystem with a next compressed data when the quality is greater than apredetermined threshold value.
 6. The computer-implemented method ofclaim 1, further comprising: measuring a size of the compressed data;and if the compression strategy is modified, repeating the creating, thelearning, and the sending to confirm that the modified compressionstrategy creates compressed data including a minimum size by comparing anew size to the measured size, wherein the compressed data is createdwith a minimum size with an effort level of decoding, the effort levelof decoding is dependent on a number of computing steps of the secondsystem, and wherein the feedback is sent to the first system if therecreated input and the input do not match.
 7. A computer programproduct for compression, the computer program product comprising acomputer-readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computer to causethe computer to perform: creating compressed data via a first systemfrom input data; sending information to a second system detailing acompression strategy for the compressed data; learning, via the secondsystem, from the information how to recreate the input to the firstsystem using the compressed data; decompressing, via the second system,the compressed data to a recreated input; comparing the input data tothe first system with the recreated input in an iterative loop; based ona result of the comparing and the iterative loop of the comparing,modifying the information at each iterative loop how to recreate theinput such that the input data to the first system matches the recreatedinput; and sending a feedback to the first system based on the recreatedinput by the second system, wherein the first system teaches the secondsystem how to recreate the input simultaneously while the second systemteaches the first system an effectiveness of the teaching that the firstsystem provides to the second system, further comprising measuring aquality of the recreated input by the second system to send a feedbackto the first system to adjust the information for the compressionstrategy, wherein the information is not sent to the second system witha next compressed data when the quality is greater than a predeterminedthreshold value, wherein the first system and the second system aretrained by the learning and feedback without training data and/orpre-annotated data, wherein the input data comprises a set ofunstructured data of special types having multiple categories toclassify each of the special types into one of the multiple categories,and wherein the first system and the second system comprise a neuronalnetwork or a cognitive expert.
 8. The computer program product of claim7, further comprising: measuring a size of the compressed data; and ifthe compression strategy is modified, repeating the creating, thelearning, and the sending to confirm that the modified compressionstrategy creates compressed data including a minimum size by comparing anew size to the measured size.
 9. The computer program product of claim7, wherein the compressed data is created with a minimum size with aneffort level of decoding, the effort level of decoding is dependent on anumber of computing steps of the second system.
 10. The computer programproduct of claim 7, wherein the recreated input by the second system iscompared with the input to the first system, and the feedback is sent tothe first system if the recreated input and the input do not match. 11.A compression system, said system comprising: a processor; and a memory,the memory storing instructions to cause the processor to perform:creating compressed data via a first system from input data; sendinginformation to a second system detailing a compression strategy for thecompressed data; learning, via the second system, from the informationhow to recreate the input to the first system using the compressed data;decompressing, via the second system, the compressed data to a recreatedinput; comparing the input data to the first system with the recreatedinput in an iterative loop; based on a result of the comparing and theiterative loop of the comparing, modifying the information at eachiterative loop how to recreate the input such that the input data to thefirst system matches the recreated input; and sending a feedback to thefirst system based on the recreated input by the second system, whereinthe first system teaches the second system how to recreate the inputsimultaneously while the second system teaches the first system aneffectiveness of the teaching that the first system provides to thesecond system, further comprising measuring a quality of the recreatedinput by the second system to send a feedback to the first system toadjust the information for the compression strategy, wherein theinformation is not sent to the second system with a next compressed datawhen the quality is greater than a predetermined threshold value,wherein the first system and the second system are trained by thelearning and feedback without training data and/or pre-annotated data,wherein the input data comprises a set of unstructured data of specialtypes having multiple categories to classify each of the special typesinto one of the multiple categories, and wherein the first system andthe second system comprise a neuronal network or a cognitive expert. 12.The system of claim 11, embodied in a cloud-computing environment.